气候变化领域集成服务门户(CCPortal): CMIP5: a Monte Carlo assessment of changes in summertime precipitation characteristics under RCP8.5-sensitivity to annual cycle fidelity; overconfidence; and gaussianity

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DOI	10.1007/s00382-019-05082-8
	CMIP5: a Monte Carlo assessment of changes in summertime precipitation characteristics under RCP8.5-sensitivity to annual cycle fidelity; overconfidence; and gaussianity
	Sperber K.R.; Annamalai H.; Pallotta G.
发表日期	2020
ISSN	0930-7575
起始页码	1699
结束页码	1727
卷号	54
英文摘要	Using 5-day averaged precipitation from all initial condition realizations of 33 CMIP5 models for the Historical and RCP8.5 scenarios, we performed an assessment of summer precipitation in terms of amount, onset date, withdrawal date, and length of season using probability distributions of interannual anomalies. Climate change projections were generated using all models, one model per modelling group to account for overconfidence, and sub-selecting models on annual cycle fidelity. Compared to using all models, sub-selecting on annual cycle fidelity has a large impact on the climate change perturbation of the fractional change in precipitation, with differences between the two projections of up to ± 50%, especially in the tropics and subtropics. Sensitivity testing indicates that the Gaussian t-test and the non-parametric Mann–Whitney U-test (the latter using Monte Carlo sampling) yield consistent results for assessing where the climate change perturbation is significant at the 1% level, even in cases where skewness and excess kurtosis indicate non-Gaussian behavior. Similarly, in terms of climate change induced perturbations to below-normal, normal, and above-normal categorical probabilities, the Gaussian results are typically consistent with the non-parametric estimates. These sensitivity results promote the use of Gaussian statistics to present global maps of the lower-bound and upper-bound of the climate change response, given that the non-parametric calculation of confidence intervals would otherwise not be tractable in a desktop computing environment due to its CPU intensive requirement. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	Annual cycle skill; Anthropogenic climate change; CMIP5; Monte Carlo sampling; Precipitation
语种	英语
scopus关键词	annual variation; anthropogenic effect; climate change; climate modeling; climate variation; confidence interval; Monte Carlo analysis; precipitation assessment; sensitivity analysis; summer; weather forecasting
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/145601
作者单位	Lawrence Livermore National Laboratory, PCMDI, P. O. Box 808, L-103, Livermore, CA 94551, United States; Department of Oceanography, International Pacific Research Center, University of Hawai’i at Manoa, Honolulu, HI, United States
推荐引用方式 GB/T 7714	Sperber K.R.,Annamalai H.,Pallotta G.. CMIP5: a Monte Carlo assessment of changes in summertime precipitation characteristics under RCP8.5-sensitivity to annual cycle fidelity; overconfidence; and gaussianity[J],2020,54.
APA	Sperber K.R.,Annamalai H.,&Pallotta G..(2020).CMIP5: a Monte Carlo assessment of changes in summertime precipitation characteristics under RCP8.5-sensitivity to annual cycle fidelity; overconfidence; and gaussianity.Climate Dynamics,54.
MLA	Sperber K.R.,et al."CMIP5: a Monte Carlo assessment of changes in summertime precipitation characteristics under RCP8.5-sensitivity to annual cycle fidelity; overconfidence; and gaussianity".Climate Dynamics 54(2020).